Prediction of Thermal Conductivity of EG–Al2O3 Nanofluids Using Six Supervised Machine Learning Models

Abstract

Accurate prediction of the thermal conductivity of ethylene glycol (EG) and aluminum oxide (Al2O3) nanofluids is crucial for improving the utilization rate of energy in industries such as electronics cooling, automotive, and renewable energy systems. However, current theoretical models and simulations face challenges in accurately predicting the thermal conductivity of EG–Al2O3 nanofluids due to their complex and dynamic nature. To that end, this study develops several supervised ML models, including artificial neural network (ANN), decision tree (DT), gradient boosting decision tree (GBDT), k-nearest neighbor (KNN), multi-layer perceptron (MLP), and extreme gradient boosting (XGBoost) models, to predict the thermal conductivity of EG–Al2O3 nanofluids. Three key parameters, particle size (D), temperature (T), and volume fraction (VF) of EG–Al2O3 nanoparticles, are considered as input features for modeling. Furthermore, five indices combining with regression graphs and Taylor diagrams are used to evaluate model performance. The evaluation results indicate that the GBDT model achieved the highest performance among all models, with mean squared errors (MSE) of 6.7735 × 10−6 and 1.0859 × 10−5, root mean squared errors (RMSE) of 0.0026 and 0.0033, mean absolute errors (MAE) of 0.0009 and 0.0028, correlation coefficients (R2) of 0.9974 and 0.9958, and mean absolute percent errors (MAPE) of 0.2764% and 0.9695% in the training and testing phases, respectively. Furthermore, the results of sensitivity analysis conducted using Shapley additive explanations (SHAP) demonstrate that T is the most important feature for predicting the thermal conductivity of EG–Al2O3 nanofluids. This study provides a novel calculation model based on artificial intelligence to realize an innovation beyond the traditional measurement of the thermal conductivity of EG–Al2O3 nanofluids.